Operators of telecommunication systems are interested in knowing as much as possible about their customers, i.e. about the users of telecommunication services. This knowledge enables the operator to customize services, to render marketing and advertising more efficiently, to identify and target important or influential users, to share the knowledge with third parties, just to mention a few examples. The use of social network analysis (SNA) algorithms is a way of gaining the desired knowledge and various algorithms and analysis methods are available for extracting and compiling data about the users.
By using such SNA algorithms, the individual behavior of a user in the telecommunication system and his interaction with other users can be analyzed. Data available from Call Data Records (CDR) may be used as input to the SNA algorithms. The CDR comprises information about made calls, calling and called parties, time of day, duration, location, type of service etc.
The amount of traffic in the telecommunication systems is increasing rapidly, and billions of calls are made every month giving huge amount of data in the CDRs. Further, the number of SNA algorithms for finding valuable information about the social network between the users is also growing.
The above described data mining is challenging in several aspects. The operator would like to obtain the most relevant information and has to choose, among the various available SNA algorithms, the algorithm that best meets the intended goal. Further, the processing of the vast amount of data is highly resource demanding and efficient data handling is required.